Breast cancer is the leading cause of death among women 40 to 44 years old, and is one of the leading causes of death in women of age 30 or more. According to the American Cancer Society, it is anticipated that more than 200,000 new cases of breast cancer will be developed in 2006 with approximately 40,000 estimated deaths. “Cancer Facts & Figures 2006, surveillance research,” American Cancer Society, Ed.: http://www.cancer.org/, 2006, pp. 4. Breast cancer results from uncontrolled growth of abnormal cells in the breast. The ability of the cancer cells to multiply continuously and spread from the breast to other organs identifies it as “malignant,” and potentially life threatening. Due to the rapid growth within a limited space, the cells of the breast cancer accumulate and form a lump that is stiffer than normal. Moreover, due to its irregular growth and extension to the surrounding tissues, malignant tumors usually have irregular borders (e.g., star-shaped).
It is well known that the mechanical properties of tumors are different than those of surrounding normal tissue; for example, some cancerous breast and prostate tumors are harder than normal tissue, or even benign tumors. T. A. Krouskop, T. M. Wheeler, F. Kallel, B. S. Garra, T. Hall, “The elastic moduli of breast and prostate tissues under compression,” Ultrason. Imaging, vol. 20, pp. 151-159, 1998. This fact led to the use of tissue stiffness to detect tumors as back as in the time of Hippocrates (460-370 BC), who used manual palpation as a way to detect breast tumors in their early stages. Moreover, because palpation is simple, it is being used today for early cancer detection in the prostate and breast. G. F. Carvalhal, D. S. Smith, D. E. Mager, C. Ramos, W. J. Catalona, “Digital rectal examination for detecting prostate cancer at prostate specific antigen levels of 4 ng/ml or less,” J. Urol., vol. 161, pp. 835-839, 1999. Nevertheless, palpation is still a subjective procedure, and detecting tumors that are too deep or too small is still problematic.
Medical imaging modalities were introduced two decades ago and that provide the potential for deep penetration, adequate resolution, and sensitivity. Since stiffness cannot be directly measured, detection of tissue deformation with some type of compression offers an alternative to determine stiffness, particularly if some mechanical model is used. The ultrasound modality has been reportedly used to generate stiffness maps of soft tissues by a technique named elastography. J. Ophir, I. Cespedes, H. Ponnekanti, Y. Yazdi, X. Li, “Elastography: a quantitative method for imaging the elasticity of biological tissues,” Ultrason. Imaging., vol. 13, pp. 111-134, 1991. Currently, there are many techniques for elastography that estimate tissue stiffness based on deformation maps measured by ultrasound modalities. In ultrasound elastography, stiffness distribution is estimated by comparing pre- and post-compression deformation parameters of the tissues. A review of these methods can be found in L. Gao, K. J. Parker, R. M. Lerner and S. F. Levinson, “Imaging of the elastic properties of tissue-a review,” Ultrason. Med. Biol., vol. 22, pp. 959-977, 1996.
Ultrasound techniques, however, suffer from a number of drawbacks. For example, tissue motion in the axial direction causes alteration of the speckle pattern that needs to be corrected using temporal stretching. Applying large strains, although favorably increasing the strain contrast among the different tissues, may induce irrecoverable distortion of the speckle pattern. J. Ophir, K. A. Alam, B. Garra, F. Kallel, E. E. Konofagou, T. A. Krouskop, and T. Varghese, “Elastography: Ultrasonic estimation and imaging of the elastic properties of tissues,” Proc. Inst. Mech. Eng., J. Engng in Medicine, Vol. 213, pp. 203-233, 1999. In addition, the off-axis and elevational tissue motion can severely corrupt the axial-strain estimation by inducing decorrelation noise which requires sophisticated correction. F. Kallel, T. Varghese, J. Ophir and M. Bilgen, “The non-stationary strain filter in elastography. Part II—lateral and elevational decorrelation,” Ultrason. Med. Biol., vol. 23, pp. 1357-1369, 1997.
Magnetic Resonance Imaging (MRI) has been introduced as a convenient modality for measuring tissue deformation that can be used to estimate tissue stiffness. N. F. Osman, “Detecting stiff masses using strain-encoded (SENC) imaging,” Magn. Reson. Med, vol. 49, pp. 605-608, 2003; C. J. Lewa, J. D. De-Certaines, “MR Imaging of viscoelastic properties,” J Magn. Res., vol. 5, pp. 242-244, 1995; T. L. Chenevert, A. R. Skovoroda, M. O'Donnel, S. Y. Emelianov, “Elasticity reconstructive imaging by means of stimulated echo MRI,” Magn. Reson. Med., vol. 39, pp. 482-490, 1998; and R. Muthupillai, D. J. Lamos, P. J. Rossman, J. F. Greenleaf, A. Manduca, R. L. Ehman, “Magnetic resonance elastography by direct visualization of acoustic strain waves,” Science, vol. 269, pp. 1854-1857, 1995.
MRI can provide features that are difficult or even impossible to implement in ultrasound. For example, MRI-based techniques are capable of direct encoding of 3-D tissue motion with better resolution and SNR as compared to ultrasound techniques. Two main approaches can be utilized in MRI to encode the tissue motion: phase encoding and magnitude encoding. Examples of the first approach include phase-contrast techniques, displacement encoding using stimulated echoes (DENSE) and fast Harmonic Phase (fast HARP) MRI. T. L. Chenevert, A. R. Skovoroda, M. O'Donnel, S. Y. Emelianov, “Elasticity reconstructive imaging by means of stimulated echo MRI,” Magn. Reson. Med., vol. 39, pp. 482-490, 1998; R. Muthupillai, D. J. Lamos, P. J. Rossman, J. F. Greenleaf, A. Manduca, R. L. Ehman, “Magnetic resonance elastography by direct visualization of acoustic strain waves,” Science, vol. 269, pp. 1854-1857, 1995; T. G. Reese, D. A. Feinberg, J. Dou, V. J. Wedeen, “Phase contrast MRI of Myocardial 3D strain by encoding contiguous slices in a single shot,” Magn Reson Med, vol. 47, pp. 665-676, 2002; D. Kim, F. H. Epstein, W. D. Gilson, L. Axel, “Increasing the Signal-to-Noise Ratio in DENSE MRI by Combining Displacement-Encoded Echoes stimulated echoes in cardiac functional MRI,” Magn. Reson. Med., vol. 52, pp. 188-192, 2004; and S. Sampath, J. A. Derbyshire, N. F. Osman, E. Atalar, J. L. Prince, “Real-time imaging of cardiac strain using an ultra-fast HARP sequence,” in Proc. 9th ISMRM, p. 111, 2001.
The second approach includes MR tagging. E. A. Zerhouni, D. M. Parish, W. Rogers, A. Yang, E. P. Shapiro, “Human heart: tagging with MR imaging—a method for noninvasive assessment of myocardial motion,” Radiology, vol. 169, pp. 59-63, 1988; and L. Axel, L. Dougherty, “MR imaging of motion with spatial modulation of magnetization,” Radiology, vol. 171, pp. 841-845, 1989. An overview of these two approaches/techniques including the advantages and possible disadvantages can be found in C. Ozturk, J. A. Derbyshire, E. R. McVeigh, “Estimating motion from MRI data,” IEEE proceedings. Vol. 91. pp. 1627-1648, 2003.
In general, current MRI techniques require multiple compression cycles in order to acquire images with different imaging parameters or to increase the SNR of the images. In addition to the unavoidable prolonged scan times, multiple compressions require a special device to produce exactly the same compression in every cycle, which may hinder the clinical use of these techniques. Moreover, using the phase information to encode the tissue motion necessitates the acquisition of an extra set of phase reference images to correct for the offset phase resulting from BO inhomogeneity. Although the acquisition of a phase reference map is not troublesome when using multiple compression cycles, it constitutes a barrier towards the use of a single compression. T. L. Chenevert, A. R. Skovoroda, M. O'Donnel, S. Y. Emelianov, “Elasticity reconstructive imaging by means of stimulated echo MRI,” Magn. Reson. Med., vol. 39, pp. 482-490, 1998; R. Muthupillai, D. J. Lamos, P. J. Rossman, J. F. Greenleaf, A. Manduca, R. L. Ehman, “Magnetic resonance elastography by direct visualization of acoustic strain waves,” Science, vol. 269, pp. 1854-1857, 1995; T. G. Reese, D. A. Feinberg, J. Dou, V. J. Wedeen, “Phase contrast MRI of Myocardial 3D strain by encoding contiguous slices in a single shot,” Magn Reson Med, vol. 47, pp. 665-676, 2002; D. Kim, F. H. Epstein, W. D. Gilson, L. Axel, “Increasing the Signal-to-Noise Ratio in DENSE MRI by Combining Displacement-Encoded Echoes stimulated echoes in cardiac functional MRI,” Magn. Reson. Med., vol. 52, pp. 188-192, 2004; and S. Sampath, J. A. Derbyshire, N. F. Osman, E. Atalar, J. L. Prince, “Real-time imaging of cardiac strain using an ultra-fast HARP sequence,” in Proc. 9th ISMRM, p. 111, 2001.
MR tagging encodes the tissue motion by Marking the tissue with alternating bright and dark tag lines than can be tracked and hence depends mainly on the image intensity not the phase. The MR tagging techniques do not require the correction of the background phases, however, because the k-space of a tagged image has a large bandwidth (due to the modulation of the intensity with a highly alternating pattern), acquisition of such images takes longer time than phase contrast or stimulated echo based techniques. Strain-Encoded (SENC) MRI is another technique that uses image intensity to encode the tissue motion. N. F. Osman, S. Sampath, E. Atalar, J. L. Prince, “Imaging Longitudinal Cardiac Strain on Short-Axis Images Using Strain-Encoded (SENC) MRI,” Magn. Reson. Med., vol. 46, pp. 324-334, 2001. Unlike conventional MR tagging techniques, SENC MRI is based on stimulated echo acquisition and thus enables rapid acquisition of motion-encoded images. Moreover, generation of strain maps from the acquired images is done faster than the analysis of MR tagged images (including the HARP technique).
Contrast enhanced MR imaging is a sensitive tool to detect breast cancer (a sensitivity of 100% was reported; W. Nunes, M. D. Schnall, and S. G. Orel, “Update of breast MR imaging architectural interpretation model,” Radiology, vol. 219, pp. 484-94, 2001). Unfortunately, the specificity (wrong-positive cases) of MRI is reported to be low and highly dependent on the imaging, processing, and interpretation technique. For example, specificity values from 37%] to 80% can be found in the literature. Therefore, the challenge in MR breast imaging is to develop methods to minimize false positives and to more easily evaluate and/or localize malignant tumors. L. Esserman, “Integration of imaging in the management of breast cancer,” J Clin Oncol, vol. 23, pp. 1601-2, 2005.
From many researchers' point of view, data fusion of images acquired from several imaging techniques can be used to increase the specificity of diagnosing breast cancer. M. A. Jacobs, R. Ouwerkerk, A. C. Wolff, V. Steams, P. A. Bottomley, P. B. Barker, P. Argani, N. Khouri, N. E. Davidson, Z. M. Bhujwalla, and D. A. Bluemke, “Multiparametric and multinuclear magnetic resonance imaging of human breast cancer: current applications,” Technol Cancer Res Treat, vol. 3, pp. 543-50, 2004; M. A. Jacobs, P. B. Barker, D. A. Bluemke, C. Maranto, C. Arnold, E. H. Herskovits, and Z. Bhujwalla, “Benign and malignant breast lesions: diagnosis with multiparametric MR imaging,” Radiology, vol. 229, pp. 225-32, 2003, and N. Hylton, “Magnetic resonance imaging of the breast: opportunities to improve breast cancer management,” J Clin Oncol, vol. 23, pp. 1678-84, 2005. One hypothesis is that stiffness images of the examined breast can help confirming the malignancy of a known tumor.
While a number of research efforts have been undertaken in the area of imaging the tissue stiffness using MRI, most MRI techniques require multiple compression cycles in order to acquire images with different imaging parameters or to increase the SNR of the images. Moreover, in some techniques such as the phase-contrast techniques, the use of the phase information to encode the tissue motion necessitates the acquisition of an extra set of phase reference images to correct for the offset phase resulting from BO inhomogeneity. In addition to the unavoidable prolonged scan times, multiple compressions require a special device to produce exactly the same compression in every cycle, which may hinder the clinical use of these techniques. T. L. Chenevert, A. R. Skovoroda, M. O'Donnell, and S. Y. Emelianov, “Elasticity reconstructive imaging by means of stimulated echo MRI,” Magn Reson Med, vol. 39, pp. 482-90, 1998; R. Muthupillai, D. J. Lomas, P. J. Rossman, J. F. Greenleaf, A. Manduca, and R. L. Ehman, “Magnetic resonance elastography by direct visualization of propagating acoustic strain waves,” Science, vol. 269, pp. 1854-7, 1995; T. G. Reese, D. A. Feinberg, J. Dou, and V. J. Wedeen, “Phase contrast MRI of myocardial 3D strain by encoding contiguous slices in a single shot,” Magn Reson Med, vol. 47, pp. 665-76, 2002; D. Kim, F. H. Epstein, W. D. Gilson, and L. Axel, “Increasing the signal-to-noise ratio in DENSE MRI by combining displacement-encoded echoes,” Magn Reson Med, vol. 52, pp. 188-92, 2004; and A. Manduca, T. E. Oliphant, M. A. Dresner, J. L. Mahowald, S. A. Kruse, E. Amromin, J. P. Felmlee, J. F. Greenleaf, and R. L. Ehman, “Magnetic resonance elastography: non-invasive mappinsg of tissue elasticity,” Med Image Anal, vol. 5, pp. 237-54, 2001.
It thus would be desirable to provide a compression device, an integrated imaging system embodying such a device and related methods for magnetic resonance imaging that would improve tissue contrast between normal and abnormal tissue. It would be particularly desirable to provide such a device, system and method that could acquire image data during one compression cycle and prior to recovery of magnetization in comparison to prior art devices, systems and methods. It also would be desirable to provide such a device, system and method in which the tissue compression and acquisition of image data can be controlled in such a way as to minimize manual intervention and control by a clinician or technician. Such compression devices preferably would be simple in construction and such methods would be less involved than conventional methods.